Mechanical and Tribological Behavior of Nitrided AISI/SAE 4340 Steel Coated with NiP and AlCrN
Bibliographic record
Abstract
In this study, novel surface engineering strategies to improve the wear performance of AISI 4340 were investigated. The strategies were as follows: (i) NiP deposition on a previously nitrided steel substrate, followed by NiP interdiffusion heat treatment at either 400 °C or 610 °C (referred to as duplex treatment); (ii) the deposition of AlCrN PVD coating on NiP layers on a previously nitrided steel substrate (referred to as triplex treatment). Prior to the deposition of AlCrN, the NiP was subjected to the interdiffusion heat treatment at either 400 °C or 610 °C. These strategies were compared with the performance of the AlCrN coating directly applied on nitrided steel. To characterize the microstructural features of each layer, X-ray diffraction (XRD) and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) analysis were conducted. We also carried out mechanical and tribological behavior assessments. The tribological tests were carried out using a ball-on-disc tribometer under a constant load of 20 N and a tangential speed of 25 cm/s; cemented carbide spheres with a diameter of 6 mm were the counterpart body. The friction coefficient was continuously monitored throughout the tests. The results reveal that the wear mechanism for the AlCrN coating is predominantly oxidative. The most wear-resistant surface architecture was the one comprising AlCrN over the NiP layer subjected to interdiffusion heat treatment at either 400 °C or 610 °C.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".